Meeting the Challenge of Populism to Children’s Rights: The Value of Human Rights Education
Bibliographic record
Abstract
Abstract This article analyses the rise of the new right-wing, nationalistic, xenophobic, and authoritarian populism as a challenge to children’s human rights. Informed by human needs theory, it situates the new populism in the context of globalization, economic grievances, and cultural resentment and backlash against out-groups. Fuelling the rise in support for populism has been growing existential insecurity combined with a lack of effective education on human rights. The outcome, as shown in countries where populism has come into power, has been a threat and an attack on the human rights of children, as described in the UN Convention on the Rights of the Child. An important means of meeting the challenge of populism, we contend, is comprehensive and robust human rights education in schools, underpinned by education on children’s rights. As called for by the UN Committee on the Rights of the Child, children’s rights education needs to be integrated into school curricula, policies, practices, teaching materials, and teacher training. Models of human rights education in schools are available and studies have shown positive results in promoting knowledge, understanding, and support for human rights. As described by the United Nations, through providing education about, through, and for human rights, the ultimate goal—yet to be realized—is to advance a culture of human rights. Such a culture would serve as a counter to populism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.077 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".